Apresentar o software R como uma ferramenta auxiliar para desenvolver análises estatísticas.
## tibble [231 × 62] (S3: tbl_df/tbl/data.frame)
## $ Campanha : num [1:231] 1 1 1 1 1 1 1 1 1 1 ...
## $ Site : chr [1:231] "A1" "A2" "A3" "A4" ...
## $ Area : chr [1:231] "A" "A" "A" "A" ...
## $ latitude : num [1:231] 494547 484554 534788 531567 529361 ...
## $ UTM Y : num [1:231] 8033813 8019361 8010029 8014250 8034993 ...
## $ Profundidade : num [1:231] 18 13 20 5 8 10 30 23 20 68 ...
## $ Data : chr [1:231] "43424" "43422" "43426" "43427" ...
## $ mesano : chr [1:231] "N18" "N18" "N18" "N18" ...
## $ Estrato : chr [1:231] "fundo" "fundo" "fundo" "fundo" ...
## $ Prochloro_cito : num [1:231] 7.37 5.42 3.66 5.57 87.14 ...
## $ Synecho_cito : num [1:231] 101.9 32.6 30.5 49.4 19.7 ...
## $ Eucari_auto_cito : num [1:231] 2 1.4 3.75 2.73 0.74 1.98 1.56 1.61 1.27 6.82 ...
## $ Bact_hete : num [1:231] 325 230 250 329 484 ...
## $ Eucari_hete : num [1:231] 12.1 6.9 5.72 7.48 1.68 ...
## $ Riq_cito_auto : num [1:231] 84 126 134 102 78 85 111 70 77 146 ...
## $ Div_cito_auto : num [1:231] 24.1 32.7 35.4 28.5 26 ...
## $ protistas_5_15um : num [1:231] NA 1550 190 NA NA ...
## $ fito_maior_15um : num [1:231] NA 28.8 14.9 NA NA ...
## $ detrito : num [1:231] NA 970 566 NA NA ...
## $ Cl_a : num [1:231] 0.439 0.34 0.22 0.28 0.16 ...
## $ Cl_b : num [1:231] 0.1039 0.0448 0.0448 0.0606 0.02 ...
## $ Cl_c1c2 : num [1:231] 0.0415 0.037 0.0232 0.0278 0.0221 ...
## $ Feofitina_a : num [1:231] 0.0625 0.0536 0.0367 0.0325 0.0628 ...
## $ DvCl_a : num [1:231] 0 0 0 0 0.0453 ...
## $ Cl_b/Cl_a : num [1:231] 0.237 0.132 0.204 0.216 0.125 ...
## $ Cl_c1c2/Cl_a : num [1:231] 0.0945 0.1087 0.1053 0.0992 0.1382 ...
## $ Feofitina_a/Cl_a : num [1:231] 0.143 0.157 0.167 0.116 0.392 ...
## $ DvCl_a/Chla : num [1:231] 0.03 0.0514 0 0.0778 0.2829 ...
## $ Bacillariophyta : num [1:231] NA 10.66 4.26 NA NA ...
## $ Dinoflagellata : num [1:231] NA 3.803 0.421 NA NA ...
## $ Cyanophyceae : num [1:231] NA 0.501 0 NA NA ...
## $ Coccolithophyceae: num [1:231] NA 0 0.0255 NA NA ...
## $ Cryptophyceae : num [1:231] NA 0 0 NA NA ...
## $ Chlorophyceae : num [1:231] NA 0 0 NA NA 0 0 NA 0 0 ...
## $ Dictyochophyceae : num [1:231] NA 0 0 NA NA 0 0 NA 0 0 ...
## $ Thecofilosea : num [1:231] NA 0 0 NA NA 0 0 NA 0 0 ...
## $ Euglenophyceae : num [1:231] NA 0 0 NA NA 0 0 NA 0 0 ...
## $ Choanoflagellatea: num [1:231] NA 0 0 NA NA 0 0 NA 0 0 ...
## $ flagelados : num [1:231] NA 0.543 0 NA NA ...
## $ Foraminifera : num [1:231] NA 0 0.0511 NA NA ...
## $ Ciliofora : num [1:231] NA 0.125 0 NA NA ...
## $ Outros : num [1:231] NA 0.293 0 NA NA ...
## $ S_microfito : num [1:231] NA 24 25 NA NA 32 40 NA 34 18 ...
## $ D_microfito : num [1:231] NA 2.38 2.83 NA NA ...
## $ H_microfito : num [1:231] NA 2.72 2.13 NA NA ...
## $ S_microfito_ciano: num [1:231] NA 26 27 NA NA 34 43 NA 36 20 ...
## $ D_microfito_ciano: num [1:231] NA 1.43 1.5 NA NA ...
## $ H_microfito_ciano: num [1:231] NA 0.414 0.342 NA NA ...
## $ IPAR : num [1:231] NA NA NA NA NA NA NA NA NA NA ...
## $ SST : num [1:231] NA NA NA NA NA NA NA NA NA NA ...
## $ Kd490 : num [1:231] NA NA NA NA NA NA NA NA NA NA ...
## $ SR : num [1:231] NA NA NA NA NA NA NA NA NA NA ...
## $ Amonia : chr [1:231] "2.8773333333333331" "0.4346666666666667" "1.3086666666666666" "0.72633333333333328" ...
## $ Nitrito : num [1:231] 0.0853 0.1427 0.1213 0.0593 0.0413 ...
## $ Nitrato : chr [1:231] "0.57699999999999996" "0.51066666666666671" "0.38566666666666666" "0.22733333333333336" ...
## $ Nitrogenio Total : num [1:231] 4.49 4.57 4.09 5.3 2.02 ...
## $ Ortofosfato : num [1:231] 0.1067 0.1127 0.1317 0.1963 0.0683 ...
## $ Fosoforo Total : num [1:231] 0.271 0.134 0.249 0.238 0.199 ...
## $ Silicato : num [1:231] 1.74 1.014 1.013 1.768 0.678 ...
## $ Temperatura : num [1:231] 26.9 26.8 26.5 26.3 25.5 ...
## $ Salinidade : chr [1:231] "36.94" "36.9" "36.93" "36.97" ...
## $ Obs : chr [1:231] "limite detecção amônia 0.05, nitrito 0.01, nitrato 0.05; valores zero indica <limite detecção" NA NA NA ...
FITOMAR$Amonia <- as.numeric(FITOMAR$Amonia)
FITOMAR$Nitrato <- as.numeric(FITOMAR$Nitrato)
FITOMAR$Salinidade <- as.numeric(FITOMAR$Salinidade)
FITOMAR$Amonia## [1] 2.87733333 0.43466667 1.30866667 0.72633333 1.10133333 1.14400000
## [7] 0.24103333 0.40266667 1.82900000 2.22333333 0.46533333 NA
## [13] 2.72000000 2.42666667 0.59700000 1.52075000 2.56025000 0.77400000
## [19] 1.74425000 2.54875000 NA NA NA 1.90800000
## [25] 1.85800000 2.25475000 NA 2.34000000 0.14766667 4.53983333
## [31] 13.18516667 1.15125000 1.01075000 1.70150000 1.31950000 1.43150000
## [37] 1.45800000 0.00000000 0.00000000 1.64400000 1.29775000 NA
## [43] NA NA NA NA NA NA
## [49] NA NA NA NA NA NA
## [55] NA NA NA NA NA NA
## [61] NA NA NA NA NA NA
## [67] NA NA NA NA NA NA
## [73] NA NA NA NA NA NA
## [79] NA NA NA NA NA NA
## [85] NA NA NA NA NA NA
## [91] NA 2.02866667 0.13200000 1.52366667 1.21333333 0.35300000
## [97] 1.95766667 2.14900000 1.79600000 0.73166667 NA 0.43066667
## [103] NA NA 1.11400000 NA NA NA
## [109] NA NA NA NA NA NA
## [115] 0.31000000 NA 2.20366667 0.28133333 NA 0.30475000
## [121] 2.11850000 1.89450000 2.45900000 4.47850000 1.45025000 NA
## [127] NA NA NA NA 1.99700000 NA
## [133] NA NA NA NA 2.04950000 NA
## [139] NA NA NA NA NA NA
## [145] NA 2.40900000 NA NA NA 6.14450000
## [151] NA 2.49966667 0.07916667 4.26933333 7.93283333 NA
## [157] 1.56200000 NA NA NA NA NA
## [163] 1.03675000 NA NA NA NA NA
## [169] NA NA NA 1.43983333 NA NA
## [175] NA 1.52225000 NA 1.06433333 0.99133333 0.07600000
## [181] NA 1.67775000 0.94883333 NA NA NA
## [187] NA NA NA NA NA NA
## [193] NA NA NA NA 0.00000000 0.00000000
## [199] NA NA 0.00000000 NA 0.00000000 NA
## [205] NA 0.00000000 NA 0.00000000 0.00000000 0.00000000
## [211] 0.00000000 NA NA NA NA NA
## [217] NA NA NA NA NA NA
## [223] NA NA NA NA NA NA
## [229] NA NA NA
## [1] "Campanha" "Site" "Area"
## [4] "latitude" "UTM Y" "Profundidade"
## [7] "Data" "mesano" "Estrato"
## [10] "Prochloro_cito" "Synecho_cito" "Eucari_auto_cito"
## [13] "Bact_hete" "Eucari_hete" "Riq_cito_auto"
## [16] "Div_cito_auto" "protistas_5_15um" "fito_maior_15um"
## [19] "detrito" "Cl_a" "Cl_b"
## [22] "Cl_c1c2" "Feofitina_a" "DvCl_a"
## [25] "Cl_b/Cl_a" "Cl_c1c2/Cl_a" "Feofitina_a/Cl_a"
## [28] "DvCl_a/Chla" "Bacillariophyta" "Dinoflagellata"
## [31] "Cyanophyceae" "Coccolithophyceae" "Cryptophyceae"
## [34] "Chlorophyceae" "Dictyochophyceae" "Thecofilosea"
## [37] "Euglenophyceae" "Choanoflagellatea" "flagelados"
## [40] "Foraminifera" "Ciliofora" "Outros"
## [43] "S_microfito" "D_microfito" "H_microfito"
## [46] "S_microfito_ciano" "D_microfito_ciano" "H_microfito_ciano"
## [49] "IPAR" "SST" "Kd490"
## [52] "SR" "Amonia" "Nitrito"
## [55] "Nitrato" "Nitrogenio Total" "Ortofosfato"
## [58] "Fosoforo Total" "Silicato" "Temperatura"
## [61] "Salinidade" "Obs"
## 'data.frame': 12 obs. of 10 variables:
## $ Site : chr "A3" "A3" "A3" "A4" ...
## $ Time : chr "F20" "F20" "M20" "M20" ...
## $ Dist_Foz : num 219 219 219 221 238 ...
## $ Dist_Costa : num 59.8 59.8 59.8 54.8 44.7 ...
## $ latitude : int 534788 534788 534788 531567 529361 529361 529361 453965 441761 445422 ...
## $ Profundidade: int 20 20 24 6 8 8 8 23 22 17 ...
## $ Chlo : num 0.529 0.529 0.765 0.441 0.393 0.365 0.365 0.25 0.377 0.641 ...
## $ K490 : num 0.073 0.073 0.089 0.065 0.061 0.057 0.057 0.044 0.058 0.08 ...
## $ Ipar : num 57.8 57.8 43.8 41.3 51 ...
## $ SST : num 27.1 27.1 27.8 27.5 25.7 ...
## .
## F20 M20 O19
## 6 5 1
mutate-asFactor-renameLevels-renameVariables-asNumeric
## [1] "abr/19" "dez/18" "fev/19" "jul/19" "jun/19" "mar/15" "mar/16" "mar/17"
## [9] "mar/18" "nov/18" "set/18" "set/19"
renameLevels
varSR <- c("Site","Time","Chlo","K490","Ipar","SST", "Dist_Foz", "Dist_Costa","Profundidade")
SR1[colnames(SR1)%in%varSR]%>%head## Joining, by = c("Site", "Time")
rm(list = ls())
setwd("~/Google Drive/My Drive/Studying - Research/UTAD/Webinar-R")
load(file = "FITO_SR.rda")
require(GGally)## Loading required package: GGally
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
require(dplyr)
var1 <- c(
"Prochloro_cito", "Synecho_cito", "Eucari_auto_cito",
"Bact_hete","Eucari_hete",
"Riq_cito_auto",
"Div_cito_auto","Estrato")
FITO.SR1 <- FITO.SR%>%
dplyr::select(all_of(var1))%>%
na.omit()
ggpairs(FITO.SR1, columnLabels = var1, aes(color = Estrato), # Separate data by levels of vs
upper = list(continuous = wrap('cor', size = 3)),
lower = list(combo = wrap("facethist", bins = 30)),
diag = list(continuous = wrap("densityDiag", alpha = 0.5)),
title = "Scatterplot matrix of `FITOSR` Grouped by Estrato")ggpairs(FITO.SR1, columnLabels = var1, aes(color = Estrato,fill = Estrato), # Separate data by levels of vs
upper = list(continuous = function(data, mapping, ...){
ggally_cor(data=data,mapping = mapping,size=3)}),
lower = list(continuous = function(data, mapping, ...){
ggally_smooth(data=data,mapping = mapping,alpha=0.2)}),
diag = list(continuous = function(data, mapping, ...){
ggally_densityDiag(data=data,mapping = mapping,alpha=0.2)+
scale_color_grey()}),
title = "Scatterplot matrix of `FITOSR` Grouped by Estrato")ww <- FITO.SR%>%
dplyr::mutate(Area=substr(Site,1,1))%>%
dplyr::filter(!is.na(Area))%>%
ggplot()+
geom_boxplot(aes(x=Area,y=Riq_cito_auto,color=Estrato,fill=Estrato),
outlier.shape = NA,size=0.5,alpha=0.3)+
scale_fill_manual(values=c("dimgrey", "gray81","ghostwhite"))+
scale_color_manual(values=c("dimgrey", "gray81","ghostwhite"))+
geom_hline(aes(yintercept=150),size=0.5,color="blue")+
theme_bw()+
theme(axis.text.x = element_text(size=10,angle=0,hjust = 0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=20),
strip.text = element_text(size=18))+
labs(x="Área",
y=bquote("Riqueza")
)
wwrequire(ggrepel)
sss1 <- FITO.SR %>%
dplyr::filter(!is.na(Area) & !is.na(Time) & !is.na(Div_cito_auto))%>%
dplyr::filter(Site=="A3",Estrato=="S")%>%
dplyr::select(Div_cito_auto,Riq_cito_auto,Date,Time)
sss1[-c(5),]%>%
ggplot()+
geom_point(aes(x=Div_cito_auto,y=Riq_cito_auto,color=Date,size=Date),show.legend = FALSE) +
geom_text_repel(aes(x=Div_cito_auto,y=Riq_cito_auto,label=Time,color=Date,size=Date),show.legend = FALSE)sss1 <- FITO.SR %>%
dplyr::filter(!is.na(Area) & !is.na(Time) & !is.na(Div_cito_auto))%>%
dplyr::filter(Site=="A3",Estrato=="S")%>%
dplyr::select(Div_cito_auto,Riq_cito_auto,Date,Time)
ms <- sss1[-c(5),]%>%
ggplot()+
geom_point(aes(x=Div_cito_auto,y=Riq_cito_auto,color=Date,size=Date),show.legend = FALSE) +
geom_text_repel(aes(x=Div_cito_auto,y=Riq_cito_auto,label=Time,color=Date,size=Date),show.legend = FALSE)
library(gganimate)
u <- ms +
transition_time(as.numeric(Date))ss <- FITO.SR %>%
dplyr::filter(!is.na(Area) & !is.na(Time) & !is.na(Div_cito_auto))%>%
dplyr::filter(Site=="A3",Estrato=="S")%>%
dplyr::select(Div_cito_auto,Riq_cito_auto,Date,Time)%>%
ggplot()+
geom_point(aes(x=Div_cito_auto,y=Riq_cito_auto,color=Time,size=Date),show.legend = FALSE) +
geom_text_repel(aes(x=Div_cito_auto,y=Riq_cito_auto,label=Time,color=Time,size=Date),show.legend = FALSE)
t <- ss +
geom_path(aes(y=Riq_cito_auto, x=Div_cito_auto),size=1)+
transition_reveal(along = Date)+
labs(title = "Year: {frame_along}")
animate(t, renderer = gifski_renderer())require(plotly)
p <- sss1[-c(5),]%>%
plot_ly(
y = ~Riq_cito_auto ,
x = ~Div_cito_auto,
#size = ~Estrato,
size=~0.8,
#linetype = ~Site,
#alpha = ~Site,
color = ~Date,
#split = ~Estrato,
frame = ~paste0(sprintf("%02d", Date), " - ", Time),
text = ~paste('Diversity:',round(Div_cito_auto*1,2),'',
'Riqueza:',round(Riq_cito_auto*1,2),''),
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(
yaxis = list(
type = "log"
)
)%>%
animation_slider(
currentvalue = list(prefix = "Time: ", font = list(color="red"))
)%>%
animation_opts(
3000, redraw = FALSE
)q <- FITO.SR %>%
dplyr::mutate(Area=substr(Site,1,1))%>%
dplyr::filter(!is.na(Area) & !is.na(Time))%>%
ggplot()+
geom_point(aes(x=SST,y=Riq_cito_auto,color=Time))+
geom_smooth(aes(x=SST,y=Riq_cito_auto),method = "loess")+
facet_grid(~Area)+
theme_bw()+
theme(panel.grid.major = element_line(color = gray(.3),
linetype = "dashed", size = 0.1),
panel.background = element_rect(fill = "aliceblue"))+
theme(axis.text.x = element_text(size=12,angle=0,hjust = 0.5),
axis.text.y = element_text(size=12),
axis.title = element_text(size=20),
strip.text = element_text(size=18))+
labs(x="SST",
y="Riqueza")
qrm(list = ls())
setwd("~/Google Drive/My Drive/Studying - Research/UTAD/Webinar-R")
load(file = "FITO_SR.rda")
varSR <- c("Site","Time","Chlo","K490","Ipar","SST")
FITO.SR%>%
group_by(Estrato,Site,Time)%>%
summarise_at(varSR[-c(1:3)],c(mean,sum))require(tidyverse)
Summary2 <- Summary1%>%
dplyr::select(Estrato,Site,Time,Chlo)%>%
drop_na()%>%
spread(Site,value=Chlo)
Summary2setwd("~/Google Drive/My Drive/Studying - Research/UTAD/Webinar-R")
load(file = "FITO_SR.rda")
require(dplyr)
var.FITO <- c(
"Prochloro_cito", "Synecho_cito", "Eucari_auto_cito",
"Bact_hete","Eucari_hete",
"Riq_cito_auto",
"Div_cito_auto")
var.SR <- c("Site","Area","Time","Estrato",
'Ipar','SST','K490','Dist_Costa','Dist_Foz',
'latitude','Profundidade' )
var.Exp <- c('Ipar','SST','K490','Dist_Costa','Dist_Foz',
'latitude','Profundidade' )
var <- c(var.SR,var.FITO)
DD <- FITO.SR%>%dplyr::select(all_of(var))%>%
group_by(Site,Time,Estrato)%>%
summarise_at(c(var.FITO,var.Exp),mean,na.rm=TRUE)%>%
na.omit()%>%as.data.frame()
library(Hmisc)
cor_matrix <- rcorr(as.matrix(DD[colnames(DD)%in%c(var.FITO,var.Exp)]))
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
library(corrplot)
corrplot(cor_matrix$r)